- A
Dead ReLU
Why wrong: Dead ReLU causes neurons to output zero regardless of input, not gradient size.
- B
Exploding gradient
Why wrong: Exploding gradient causes large gradients, not small.
- C
Covariate shift
Why wrong: Covariate shift refers to changes in the input distribution, not gradient magnitude.
- D
Vanishing gradient
Correct: Vanishing gradient makes weights stop updating effectively.
Question 858 of 1,000
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning engineer notices that the gradient values in a deep network are becoming extremely small during backpropagation. What is this problem?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vanishing gradient
The vanishing gradient problem occurs when gradients become extremely small during backpropagation, especially in deep networks with many layers. This causes the weights in earlier layers to update very slowly or not at all, severely hindering training. The correct answer is D because the scenario directly describes the hallmark symptom of vanishing gradients.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dead ReLU
Why it's wrong here
Dead ReLU causes neurons to output zero regardless of input, not gradient size.
- ✗
Exploding gradient
Why it's wrong here
Exploding gradient causes large gradients, not small.
- ✗
Covariate shift
Why it's wrong here
Covariate shift refers to changes in the input distribution, not gradient magnitude.
- ✓
Vanishing gradient
Why this is correct
Correct: Vanishing gradient makes weights stop updating effectively.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between vanishing and exploding gradients by describing the symptom (small vs. large gradients) and expects candidates to recognize that vanishing gradients cause slow learning in early layers, not just any training difficulty.
Trap categories for this question
Command / output trap
Dead ReLU causes neurons to output zero regardless of input, not gradient size.
Detailed technical explanation
How to think about this question
Vanishing gradients are particularly severe with sigmoid or tanh activation functions, as their derivatives saturate near zero for large positive or negative inputs. In very deep networks, the chain rule multiplies many small derivatives, causing the gradient to decay exponentially with depth. Modern solutions include using ReLU activations, careful weight initialization (e.g., He or Xavier), batch normalization, and residual connections (skip connections) to allow gradients to flow directly through the network.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vanishing gradient — The vanishing gradient problem occurs when gradients become extremely small during backpropagation, especially in deep networks with many layers. This causes the weights in earlier layers to update very slowly or not at all, severely hindering training. The correct answer is D because the scenario directly describes the hallmark symptom of vanishing gradients.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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